Chinese Journal of Information Fusion Home About Editors Current Issue
-
CiteScore
2.46
Impact Factor
  1. Home
  2. Journals
  3. Chinese Journal of Information Fusion
  4. Volume 1, Issue 2
A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
Research Article | Published: 27 September 2024
Chinese Journal of Information Fusion Volume 1, Issue 2, 2024: 109-125
Volume 1, Issue 2, Chinese Journal of Information Fusion
Volume 1, Issue 2, 2024
Submit Manuscript Edit a Special Issue
Academic Editor
Deqiang Han
Deqiang Han
Xi'an Jiaotong University, China
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
Chinese Journal of Information Fusion, Volume 1, Issue 2, 2024: 109-125

Free to Read | Research Article | 27 September 2024
A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
by
Tongyao Yang Tongyao Yang 1
Fengbao Yang Fengbao Yang 1 *
1 School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
* Corresponding Author: Fengbao Yang, [email protected]
DOI: 10.62762/CJIF.2024.596648
Received: 29 July 2024, Accepted: 23 September 2024, Published: 27 September 2024  
Cited by: 1  (Source: Web of Science) , 1  (Source: Google Scholar)
   PDF  (11.15 MB)
Article Metrics Cite This Article
Abstract
In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional probabilistic map. An improved ant colony algorithm is proposed to efficiently generate high-quality sets of headings, facilitating the preliminary heading planning for UAVs. Then, the three-dimensional environment of the heading regions is extracted, and an improved Dung Beetle algorithm with multiple strategies is proposed to optimize the three-dimensional path in the secondary layer accurately. The efficacy and quality of the proposed path planning methodology are substantiated through comprehensive simulation analysis.
Graphical Abstract
A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace
Keywords
two-dimensional probabilistic map
trajectory planning
optimization algorithm
two-Layer path planning
Funding
This work was supported by the National Natural Science Foundation of China under Grants (61972363 and 61672472).
References
  1. Sepulveda, E., & Smith, H. (2017). Technology challenges of stealth unmanned combat aerial vehicles. The Aeronautical Journal, 121(1243), 1261-1295.
    [Google Scholar]
  2. Qin, J., & Wu, X. (2016, October). Modeling and simulation on the earliest launch time of ship-to-air missile of warship formation in cooperative air-defense. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 375-379). IEEE.
    [Google Scholar]
  3. Gao, Y., Li, D. S., & Zhong, H. (2020). A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment. Engineering Applications of Artificial Intelligence, 87, 103276.
    [Google Scholar]
  4. Zhang, R., Chai, R., Chai, S., Xia, Y., & Tsourdos, A. (2023). Design and practical implementation of a high efficiency two-layer trajectory planning method for AGV. IEEE transactions on industrial electronics, 71(2), 1811-1822.
    [Google Scholar]
  5. Bashir, N., Boudjit, S., Dauphin, G., & Zeadally, S. (2023). An obstacle avoidance approach for UAV path planning. Simulation modelling practice and theory, 129, 102815.
    [Google Scholar]
  6. Ren, Z., Rathinam, S., Likhachev, M., & Choset, H. (2022). Multi-objective path-based D* lite. IEEE Robotics and Automation Letters, 7(2), 3318-3325.
    [Google Scholar]
  7. Chong, L., Jian, L., & XueQuan, L. (2020). Static rectangle expansion A* algorithm for pathfinding. IEEE Transactions on Games, 14(1), 23-35.
    [Google Scholar]
  8. Ali, H., Xiong, G., Haider, M. H., Tamir, T. S., Dong, X., & Shen, Z. (2023). Feature selection-based decision model for UAV path planning on rough terrains. Expert Systems with Applications, 232, 120713.
    [Google Scholar]
  9. Diao, Q., Zhang, J., Liu, M., & Yang, J. (2023). A Disaster Relief UAV Path Planning Based on APF-IRRT* Fusion Algorithm. Drones, 7(5), 323.
    [Google Scholar]
  10. Zhang, J., An, Y., Cao, J., Ouyang, S., & Wang, L. (2023). UAV trajectory planning for complex open storage environments based on an improved RRT algorithm. IEEE Access, 11, 23189-23204.
    [Google Scholar]
  11. Hao, G., Lv, Q., Huang, Z., Zhao, H., & Chen, W. (2023). Uav path planning based on improved artificial potential field method. Aerospace, 10(6), 562.
    [Google Scholar]
  12. Liu, J., Yan, Y., Yang, Y., & Li, J. (2024). An Improved Artificial Potential Field UAV Path Planning Algorithm Guided by RRT Under Environment-aware Modeling: Theory and Simulation. IEEE Access.
    [Google Scholar]
  13. Wu, J., Wang, H., Wang, Y., & Liu, Y. (2021). UAV reactive interfered fluid path planning. Acta Automatica Sinica, 47(1), 1-16.
    [Google Scholar]
  14. Yang, T., Yang, F., & Li, D. (2024). A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density. Drones, 8(5), 205.
    [Google Scholar]
  15. Dewangan, R. K., Shukla, A., & Godfrey, W. W. (2019). Three dimensional path planning using Grey wolf optimizer for UAVs. Applied Intelligence, 49, 2201-2217.
    [Google Scholar]
  16. Hu, K., & Mo, Y. (2024). A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour. Measurement Science and Technology, 35(4), 046203.
    [Google Scholar]
  17. Zheng, R. Z., Zhang, Y., & Yang, K. (2022). A transfer learning-based particle swarm optimization algorithm for travelling salesman problem. Journal of Computational Design and Engineering, 9(3), 933-948.
    [Google Scholar]
  18. Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305-7336.
    [Google Scholar]
  19. Miao, C., Chen, G., Yan, C., & Wu, Y. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering, 156, 107230.
    [Google Scholar]
  20. Shen, Q., Zhang, D., Xie, M., & He, Q. (2023). Multi-strategy enhanced dung beetle optimizer and its application in three-dimensional UAV path planning. Symmetry, 15(7), 1432.
    [Google Scholar]
  21. Lyu, L., Jiang, H., & Yang, F. (2024). Improved Dung Beetle Optimizer Algorithm with Multi-Strategy for global optimization and UAV 3D path planning. IEEE Access.
    [Google Scholar]
  22. Jiachen, H., & Li-hui, F. (2024). Robot path planning based on improved dung beetle optimizer algorithm. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(4), 235.
    [Google Scholar]
  23. Wai, R. J., & Prasetia, A. S. (2019). Adaptive neural network control and optimal path planning of UAV surveillance system with energy consumption prediction. IEEE Access, 7, 126137-126153.
    [Google Scholar]
  24. Liu, Y., Zheng, Z., Qin, F., Zhang, X., & Yao, H. (2022). A residual convolutional neural network based approach for real-time path planning. Knowledge-Based Systems, 242, 108400.
    [Google Scholar]
  25. Luo, X., Wang, Q., Gong, H., & Tang, C. (2024). UAV path planning based on the average TD3 algorithm with prioritized experience replay. IEEE Access.
    [Google Scholar]
  26. Zhou, C., Gu, S., Wen, Y., Du, Z., Xiao, C., Huang, L., & Zhu, M. (2020). The review unmanned surface vehicle path planning: Based on multi-modality constraint. Ocean Engineering, 200, 107043.
    [Google Scholar]
  27. Huang, Z. M., Chen, W. N., Li, Q., Luo, X. N., Yuan, H. Q., & Zhang, J. (2020). Ant colony evacuation planner: An ant colony system with incremental flow assignment for multipath crowd evacuation. IEEE Transactions on Cybernetics, 51(11), 5559-5572.
    [Google Scholar]
  28. Yang, T., Yang, F., & Li, D. (2022). An Air Target Course Prediction Method Based on Sub-Regions Divide and Conquer With Double Variable Weight. IEEE Access, 10, 117871-117885.
    [Google Scholar]
Cite This Article
APA Style
Yang, T. & Yang, F. (2024). A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast airspace. Chinese Journal of Information Fusion, 1(2), 109–125. https://doi.org/10.62762/CJIF.2024.596648
Article Metrics
Citations:

Google Scholar
1

Crossref

0

Scopus

1

Web of Science

1
Article Access Statistics:
Views: 881
PDF Downloads: 170
Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Institute of Emerging and Computer Engineers (IECE) or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Chinese Journal of Information Fusion

Chinese Journal of Information Fusion

ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.